Demand Planning: 10-Wk Implementation


A cross-market, ML-based implementation to predict, share, and analyze data to drive business performances

This 10-week implementation offering provides a quick on-ramp to design, develop, and deploy a solution based on Azure AI and Microsoft Power BI to predict, share, and analyze data to drive business performances, thank's to dashboards and reports for your key decision makers. In today’s business environment, companies can only step ahead of competition by promptly adapting to market changes and trends and consequently save time, effort, and money. BeanTech’s scalable Demand Planning solution offers a predictive ML engine, a dedicated environment to enhance collaboration between the different company’s department and a Business Intelligence tool to manage, analyze and relate the data elaborated by the engine with the ones relevant for your business. The implementation meets the needs of several businesses, ranging from medium to large ones, and of different sectors: from food & beverage to manufacturing, from retail to healthcare. It offers a scalable infrastructure, divided into three modules:

  • A core module, based on Azure Machine Learning, enables historical data elaboration and data correlation with statistical algorithms.
  • A dedicated web-based portal enhances information sharing and collaboration between different company’s departments, thus simplifying the management of inter-company knowledge and making sure everyone has the right data to make the right decision.
  • A Business Analytics module, based on Microsoft Power BI, correlates the data with business-specifics KPIs and gives you access to advanced reports and insights for a complete overview on your business. Based on the department in which it is used, the implementation leads to several benefits: inside a sales department, for instance, it can help improving the overall customers’ satisfaction by predicting market trends and needs; if applied in logistics, it can help reducing warehouse stock by structuring a more effective stock distribution.